Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.

The vigorous development of artificial intelligence (AI) is a key initiative to drive technological innovation, achieve industrial upgrading, and enhance the resilience of the Chinese economy. As one of the important applications of AI, industrial robots have transformed the production modes of traditional manufacturing industries by leveraging digital technologies and big data algorithms. Based on data from Chinese listed manufacturing companies on the A-share market between 2012 and 2019, this study explores the impact of industrial robot applications on the resilience of Chinese manufacturing firms. The research findings demonstrate that industrial robot applications significantly enhance firm resilience, which remains robust after a series of robustness tests. Mechanism analysis reveals that industrial robot applications enhance firm resilience through two mechanisms: Improving labor productivity and promoting technological innovation. Heterogeneity analysis indicates that the positive impact of industrial robot applications on firm resilience is more pronounced in non-state-owned enterprises, firms with high technological compatibility, firms with high product technological complexity and regions with higher levels of marketization. This study adds new evidence to the study of the economic consequences of artificial intelligence and expands the literature on the influencing factors of firm resilience. This study also provides theoretical support and policy insights for enhancing firm resilience through artificial intelligence, thereby enhancing the resilience of the Chinese economy.

The main purpose of this paper is using the elementary methods, the number of the solutions of some congruence equations and the properties of the classical Gauss sums to study the calculating problem of the fifth power mean of one kind two-term exponential sums, and give the exact calculating formula for it.

Enhancing the welfare of the people is one of the core goals of high-quality development in China's new era. Digital financial inclusion plays a crucial role in improving the subjective well-being of Chinese residents. Utilizing the data from the China Household Finance Survey from 2013 to 2019, and integrating city tiers with municipal digital financial inclusion indices, this paper empirically investigates the impact of digital financial inclusion development on residents' subjective well-being using the ordered Probit model. The findings indicate that the development of digital financial inclusion significantly enhances the subjective well-being of residents. In terms of dimensions, its breadth of coverage and depth of use have a positive impact on residents' well-being, while the degree of digitalization has a negative effect. Moreover, the impact of digital financial inclusion development on subjective well-being varies significantly across different relative income and educational levels. Mechanism analysis shows that the development of digital financial inclusion enhances subjective well-being through three pathways: Improving residents' financial literacy, improving economic conditions, and enhancing social security levels.

This study investigates the impact of two types of FinTech developed and utilized by banks and non-bank financial institutions on fraudulent behavior in China's A-share listed companies. Based on panel data from 2011 to 2020, the research findings suggest that both types of FinTech can suppress corporate fraud by enhancing internal control levels and external monitoring levels. Heterogeneity analysis indicates that the inhibitory effects of both FinTech types are more pronounced in companies with higher levels of digital transformation and lower levels of information disclosure. Additionally, due to differences in operating conditions, strategies, and objectives of FinTech developers, the inhibitory effect of bank FinTech is significant across all firms, whereas the effect of non-bank FinTech is only significant in high-risk firms. When distinguishing types of corporate fraud, both FinTech types significantly inhibit fraudulent activities related to information disclosure, fund utilization, and other categories. Further analysis reveals a complex interaction between the application effects of bank FinTech and non-bank FinTech. Specifically, the inhibitory effect of bank (non-bank) FinTech is significant when the development of other FinTech is high (low). By simultaneously incorporating both types of FinTech and their interaction terms, significant synergistic inhibitory effects are observed in fund misuse and other types of fraud. Finally, the results indicate that the synergistic development of both types of FinTech may introduce potential risks. In summary, this paper, by identifying the impact of FinTech development on corporate fraudulent behaviors, highlights the common characteristics and individual differences of different types of FinTech, emphasizes potential future cooperation opportunities between bank and non-bank FinTech, and points out potential risks in the development of FinTech.

Promoting enterprises to accelerate digital transformation is of great significance to enhance the core competitiveness of enterprises, empower the upgrading of traditional industries, generate new forms of business, as well as drive China's digital economy to become better and stronger. From the perspective of enterprises, this paper analyzes the antecedents of enterprises' digital transformation, constructs related indexes based on the text analysis method, proposes a two-factor theoretical model of manager characteristics and dynamic capabilities, and uses the structural equation model based on partial least squares estimation (PLS-SEM). The empirical results show that manager characteristics such as entrepreneurship, digital evangelist and coordinator, as well as corporate dynamic capabilities such as sensing, learning, integrating and coordinating, have a significantly positive role in promoting the tendency and output of digital transformation of enterprises. In addition, manager characteristics can significantly improve the level of enterprises' dynamic capabilities, and the effect of manager characteristics on enterprises' dynamic capabilities and digital transformation is moderated by managers' perception of policy uncertainty. In addition, the above effects are heterogeneous between state-owned and private enterprises, enterprises in the eastern, central and western regions, as well as enterprises in provincial and non-provincial capitals. This paper fills the research gap on the antecedents of digital transformation, and provide a feasible practical path for enterprises to cultivate managers in the digital era and improve their dynamic capabilities.

Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

Utilizing the 2015, 2017, and 2019 China Household Finance Survey (CHFS) data, combined with the income transition matrix analysis method and empirical analysis method, this study systematically investigates the impact of digital finance on income mobility and income inequality among rural households. The income transition matrix analysis reveals that rural households using digital finance have a higher probability of upward income mobility compared to those not using digital finance. Empirical research has found that digital finance significantly promotes upward income mobility and significantly reduces income inequality among rural households. The mechanism of action indicates that digital finance enhances households' income mobility by improving financial accessibility, facilitating the accumulation of development factors, and promoting off-farm employment opportunities. Furthermore, compared to middle and high-income rural households, digital finance has a greater impact on financial accessibility, development factor accumulation, and off-farm employment for low-income rural households. This consequently reduces income inequality, showcasing the inclusive growth characteristic of digital finance. Further analysis reveals that digital finance primarily impacts rural households' property income and wage income through these three pathways, ultimately promoting overall income mobility and reducing income inequality among households. Both digital payments and digital wealth management significantly contribute to upward income mobility and the reduction of income inequality among rural households, while digital lending has a negligible impact. This study provides empirical evidence to support the enhancement of policies aimed at fostering sustained income growth for rural households and optimizing the rural income distribution pattern through digital finance.

In order to explore how green technology innovation and the development of the digital economy can jointly promote green economic growth, this paper constructs a general equilibrium model that includes green technology innovation and digital transition. Combining with the real-world data at the city level in China, from the two aspects of economic growth and carbon emissions, it analyzes the impact and the mechanism of action of the digital economy collaborating with green technology innovation on green economic growth. It found that: 1) Green technology innovation has a "U-shaped" impact on economic growth and carbon emissions. That is, after exceeding a specific threshold of technological innovation level, with the continuous increase in the level of green technology innovation, the economic growth rate will continuously increase, and carbon emissions will continue to decrease. Moreover, the development of digital economy will strengthen the impact of green technology innovation, resulting in a steeper "U-shaped" relationship. 2) The development of economic digitalization has both mediating and moderating effects. Green technology innovation has a positive "U-shaped" impact on the development of the digital economy. That is, an increase in green technology innovation can promote the development of digital economy. In turn, the development of digital economy further moderates the impact of green technology innovation on economic growth and carbon emission reduction, strengthening the positive effect of green technology innovation on green economic growth. 3) The digital economy's enhancement of the impact of green technology innovation on green total factor productivity is the primary mechanism by which the digital economy, in collaboration with green technology innovation, drives green economic growth. 4) Policies to promote the development of economic digitalization need to be accompanied by higher carbon taxes. Although there are short-term economic costs, there are advantages in terms of long-term economic growth and environmental quality. This research combines the study of the green transition of economic development with that of digital transition, providing crucial theoretical support for the coordinated advancement of the green and digital transition of the economy to ensure stable economic growth.

This paper introduces digital financial capability into the intertemporal decision model, constructs a theoretical analysis framework to explore the impact mechanism of digital financial capability on household wealth accumulation, and conducts an empirical test based on the data of China Household Finance Survey (CHFS). The research shows that digital financial capability can significantly promote household wealth accumulation in China, particularly for rural households and those with low education and low wealth levels. Mechanism analysis shows that increasing financial investment returns and promoting social interaction are two channels through which digital financial capability can improve household wealth accumulation. Further analysis shows that there are structural differences in the impact of digital financial capability on household wealth accumulation, which can improve the allocation of productive assets and financial assets, and reduce the holding of housing assets and other non-financial assets. The above research conclusions provide a new perspective to explain the accumulation of household wealth in China, and also provide a reference for the formulation of relevant policies to promote common prosperity.

The implementation of the EU Battery Act has intensified the demand for recycled batteries in the market, single-channel recycling of retired power batteries can no longer fully meet the need of production, dual-channel recycling model is becoming a trend. In this paper, a single-channel retired power battery recycling model is proposed, and two dual-channel retired power battery recycling models are extended on the basis of the proposed model. The effects of changes in key parameters on the equilibrium results are analyzed under the three models. The impacts of government subsidy on supply chain members' strategic preferences and incentives to recycle under the dual-channel recycling model are explored in the extended analysis. The study shows that: 1) Changes in market size, market discount factor, refurbishment cost and price sensitivity factor will lead to adjustments in the strategies of supply chain members. Supply chain members can always benefit from increased marketing investment to increase the demand for new energy vehicles with refurbished batteries (uv). 2) Battery manufacturers (BM) and Third party battery recycling remanufacturer (RM) will benefit from the RE model under more stringent environmental policy. Although strict environmental policies will always have a negative impact on electric vehicle manufacturer (EV), choosing the RB model can help to minimize this negative impact. 3) Government subsidy provides more significant incentives for EV and RM, while the incentives for BM are very limited. 4) Strict environmental policy always reduce the profit of the supply chain, while government subsidy is beneficial to the performance of the supply chain. Without government subsidy, RB model should be chosen to improve the profit of the supply chain under lenient environmental policy, otherwise, single-channel mode is beneficial to improve the profit of the supply chain; with government subsidy, RB model is always more beneficial to the supply chain. The above analysis leads to the following management insights: Supply chain members should increase marketing investment to increase demand for uv. Strict environmental policy always discourages BM and EV from participating in battery recycling, and there is a need to develop specific incentives for EV and BM. Government subsidy can effectively incentivize EV and RM to participate in battery recycling, incentives for BM should be more diversified. This study only considered the effect of government subsidy on the performance level of supply chain members, the impact of EPR on the strategic choices of supply chain members and battery recycling will be further explored in the future.

Component design (“digitalization”), performance optimization (“optimization”), and process simulation (“simulation”) are three critical modules in the 3D printing process. “Digitalization” refers to the transformation of design drawings or pre-processed physical objects into editable digital components through means such as images, videos, and scanning processes. “Optimization” involves the application of constraints from physical fields such as mechanics and thermodynamics to enhance the performance of digital components. “Simulation” entails the digital simulation and twin modeling of physicochemical changes during the manufacturing process, based on the optimized components, to emulate real-world physical conditions. This research aims to introduce integrated modeling and algorithmic studies in design, optimization, and simulation within the phase field framework. In the “digitalization” module, we will present three-dimensional reconstruction models, repair models, and lightweight support structure design models that correspond to common data types in computer-aided design. In the “optimization” module, we will introduce a series of multi-scale, multi-physical field, and multi-material coupled topology optimization problems and their corresponding solutions. In the “simulation” module, we will discuss macroscopic (phase transition) to microscopic (grain boundary) scale coupling theories and the physical field couplings such as laser-thermal-flow-solid, in addition to simulating methods for processes like Fused Deposition Modeling (FDM) and Selective Laser Melting (SLM), integrating 3D printing parameters and techniques. The integrated research approach to design, optimization, and simulation based on the phase field model framework aims to shorten the product development cycle, enhance design efficiency, and provide a theoretical basis and algorithmic support for quality traceability and root cause analysis of 3D printed components.

Industrial policy has always played an important role in promoting industrial structure transformation and high-quality economic development. Based on the Five-Year Plan of the province level local governments and the micro-data of Chinese industrial enterprises, this paper constructs a staggered DID identification strategy to empirically analyze the impact of local key industrial policies on firms' TFP. The study found that local key industrial policies can significantly improve the TFP of enterprises through policy effects (financial subsidies, tax breaks, low-interest loans) and competitive effects. Further analysis shows that the way local key industrial policies formulated and implemented will have an important impact on the effect of industrial policies. The impact of local key industrial policies formulated combining with the regional comparative advantage, or implemented dispersedly is better. This study provides Chinese empirical evidence for the impact of industrial policies on firms' productivity, which can provide useful reference for the government to formulate and implement industrial policies and promote high-quality economic development.

In the process of low-carbon transition, enterprises require substantial financial support for related investments. Therefore, the effectiveness of carbon pricing policies depends on a well-functioning financial market. However, in reality, financial markets face various frictions that hinder the flow of capital, leading to inefficient allocation of resources. These frictions may affect corporate investment behavior, thereby weakening the implementation effects of carbon pricing policies. This paper, focusing on the issue of financing constraints, constructs an environmental-dynamic stochastic general equilibrium (E-DSGE) model incorporating a financing collateral constraint mechanism to analyze the impact of financing constraints on the effectiveness of carbon pricing policies and explores corresponding policy responses. The results show that: 1) From the perspective of environmental benefits, financing constraints weaken the "emission reduction effect" of carbon pricing policies, suppress corporate low-carbon investments, and reduce corporate emission intensity; 2) From the perspective of economic costs, financing constraints amplify the cost impact of carbon pricing on enterprises, restrict output growth, and increase the overall economic cost of the low-carbon transition; 3) Introducing carbon asset-backed loans as a complementary measure to carbon pricing policies can effectively mitigate the negative impact of financing constraints on carbon pricing policies; 4) Numerical simulation shows that financing constraints increase the proportion of carbon pricing-related costs in enterprises' total production costs from an average of 15.31% to 19.47% annually, while reducing the annual average scale of low-carbon investments by approximately 37%. Furthermore, providing more carbon asset-backed loans to high-emission enterprises can significantly enhance policy benefits. The conclusions of this paper are of great significance for improving mechanisms for green and low-carbon development and establishing a systematic climate policy framework.

In the context of the development of financial technology, we start with the complex characteristics of financial big data and elaborate on the importance of transfer learning of using multi-source data information to assist target tasks. We explain the significance of transfer learning technology in dealing with data heterogeneity from the perspective of multi-source data, and summarize the relevant concepts and methods of transfer learning technology, including data-driven and model-based transfer learning methods. In addition, this paper proposes the unified framework of transfer learning method based on generalized moment estimation (GMM), gives the effective algorithm of transfer learning, and applies the proposed method to the application of transfer learning in risk value (VaR) and risk measure based on expected quantile (expectile) under multi-source data. Then, we simulate two scenarios where samples are of insufficient or imbalanced sample sizes, respectively, in the application to personal bank credit evaluation, with tests of the prediction accuracy of three transfer learning methods, and analysis of the importance of filtering resource domain information. Finally, we described more application scenarios and development prospects of transfer learning in the financial field.

In recent years, China's food delivery industry has undergone substantial growth, driven by the rapid expansion of the platform economy and the influence of the COVID-19 pandemic. Food delivery services have not only lessened customers' sensitivity to delays associated with in-person dining but have also generated increased market demand for merchants. It is noteworthy that the majority of merchants employ a centralized operational mode, which combines food delivery and dine-in services within a single establishment. However, certain merchants opt for a decentralized approach, wherein they establish dedicated food delivery outlets exclusively handling food delivery orders while maintaining an offline restaurant. To examine the impact of food delivery channels on merchant decision-making, this study establishes a dual-channel service system operating within a congestion-prone environment. It characterizes the equilibrium strategy of customers under the two operational policies and investigates how the quality of food delivery services affects merchant profits. Furthermore, the research reveals the optimal operational approach based on varying levels of delivery quality. The key findings of the study are as follows. 1) In the case of decentralized operations, the service capacity allocated to the food delivery channel by the merchant exhibits a non-monotonic relationship with its quality. This implies that higher food delivery quality may gradually prompt the merchant to shift its focus toward the offline channel. 2) Despite the fact that higher food delivery quality has the potential to attract more customers, the study surprisingly finds that improving food delivery quality may actually reduce merchant profits in both centralized and decentralized scenarios. 3) While decentralized operations may lead to decreased order processing efficiency, adopting this approach can effectively mitigate the cannibalization effect of the food delivery channel and result in higher profits, particularly when food delivery quality is high. Consequently, centralized mode is recommended only when the food delivery quality falls within an intermediate range. Additionally, we further validated the robustness of this conclusion from various perspectives, including marginal costs and delivery fees.

Promoting mass entrepreneurship and innovation is of great significance for advancing economic structural adjustment, creating new engines of development, enhancing new driving forces for development, and pursuing a path of innovative-driven development, as well as promoting social upward mobility and fairness and justice. This study utilizes data from three rounds of the China Household Finance Survey (CHFS) conducted from 2013 to 2017 to measure the degree of opportunity inequality in China and empirically investigates its effects on resident entrepreneurship and the underlying mechanisms. The results indicate that the rise in opportunity inequality significantly stimulates the entrepreneurial motivation of resident households. For each standard deviation increase in opportunity inequality, the probability of household entrepreneurship increases by 1.14%. Mechanism analysis shows that opportunity inequality stimulates residents' pursuit of status, thereby promoting entrepreneurship among residents. Furthermore, expanded research findings reveal that digital finance strengthens the positive driving effect of opportunity inequality on entrepreneurship. This enhancement effect is only present in urban areas and is achieved by improving loan accessibility. Additionally, this study finds that livelihood-oriented fiscal expenditures also amplify the promotion effect of opportunity inequality on resident household entrepreneurship. In heterogeneous analysis, households with lower educational levels, lower total assets, and no unemployment insurance show stronger entrepreneurial motivations. Finally, this study finds that opportunity inequality suppresses the entrepreneurial performance of entrepreneurial households, indicating that government policies should focus on strengthening equal opportunities and supporting resident entrepreneurship.

With the rapid development of digital finance, digital transformation has become a strategic imperative for commercial banks. This paper employs a more comprehensive data set from China Banking Database (CBD), and examines the relationship between bank digital transformation and systemic vulnerability risks. Empirical results indicate that there is a significant inverted U-shaped relationship between the level of bank digital transformation and its own systemic vulnerability risks, and the results are robust, remaining valid even after addressing endogeneity issues. Mechanism analyses reveal that bank digital transformation indirectly affects its own systemic vulnerability risks by altering income acquisition efficiency and active risk-taking. The empirical findings of this paper have important practical implications for preventing systemic financial risks in the banking industry and understanding the balance between digital innovation and risk management.

"The adjustment of coal-based electric energy transportation" plays a significant role in enhancing the ecological environment and reducing coal consumption in recipient areas. However, it faces challenges in practice. This study examines the "Structure adjustment of coal electric energy transport" from the "Sanxi Region" to the Beijing-Tianjin-Hebei region based on real-world experiences. By employing a multi-regional CGE model, this paper quantitatively analyzes the environmental, economic, and social impacts of this adjustment on both regions. The research reveals that the primary issue with the current transition is the imbalance of interests between the sending and receiving areas. Specifically, while the Beijing-Tianjin-Hebei region benefits from improved air quality, the "Sanxi Region" suffers from negative effects on both the atmosphere and economy. Considering China's 14th Five-Year Plan environmental protection goals, this paper suggests an optimal annual growth range for coal-based electric energy transportation from 2021 to 2025 of [14\%, 27\%]. Additionally, it proposes an optimized tax rate range for joint air pollution control and an economic compensation plan. This research offers a solution path and reference for overcoming challenges in the transformation of coal-based electric energy transportation and contributes to achieving ecological objectives in the Beijing-Tianjin-Hebei region.

We study some properties of Toeplitz operators with positive operator-valued function symbols on the vector-valued exponential weighted Bergman spaces $A^p_{\varphi}(\mathcal{H})\ (1 ＜ p ＜ \infty)$. Firstly, we discuss when the Bergman projection from $L^p_{\varphi}(\mathcal{H})$ onto $A^p_{\varphi}(\mathcal{H})$ is bounded and get the dual of the vector-valued exponential weighted Bergman spaces. Secondly, we obtain several equivalent descriptions of Carleson condition to characterize the boundedness and compactness of Toeplitz operators on $A^p_{\varphi}(\mathcal{H})$. Finally, we consider the Schatten-$p$ class membership of Toeplitz operators acting on $A^2_{\varphi}(\mathcal{H})$.